Real-Time Sign Language Recognition

Cristian Amaya; Victor Murray

doi:10.1109/INTERCON50315.2020.9220241

Real-Time Sign Language Recognition

Cristian Amaya
, Victor Murray

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

4 Citas (Scopus)

Resumen

We propose an automatic system to recognize sign language using principal component analysis (PCA) and one-vs.-all support vector machines (SVM) classification. The algorithm was trained and tested using a total of 500 images of the five vowels. The method includes color information, to detect skin regions, hand segmentation, using morphological operations and filters, feature extraction in hand regions using PCA, and classification using SVM. A graphical user interface was implemented for real-time recognition. For this first approach, the system was optimized for working with the five vowels showing results of a testing accuracy above 80% and an execution time of 59 milliseconds per frame.

Idioma original	Inglés
Título de la publicación alojada	Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9781728193779
DOI	https://doi.org/10.1109/INTERCON50315.2020.9220241
Estado	Publicada - set. 2020
Publicado de forma externa	Sí
Evento	27th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020 - Virtual, Lima, Perú Duración: 3 set. 2020 → 5 set. 2020

Serie de la publicación

Nombre	Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020

Conferencia

Conferencia	27th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020
País/Territorio	Perú
Ciudad	Virtual, Lima
Período	3/09/20 → 5/09/20

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 7: Energía asequible y no contaminante

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10.1109/INTERCON50315.2020.9220241

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Amaya, C., & Murray, V. (2020). Real-Time Sign Language Recognition. En Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020 Artículo 9220241 (Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INTERCON50315.2020.9220241

Amaya, Cristian ; Murray, Victor. / Real-Time Sign Language Recognition. Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020. Institute of Electrical and Electronics Engineers Inc., 2020. (Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020).

@inproceedings{95f6434afa6b4cf7800c07b7e3bfbddf,

title = "Real-Time Sign Language Recognition",

abstract = "We propose an automatic system to recognize sign language using principal component analysis (PCA) and one-vs.-all support vector machines (SVM) classification. The algorithm was trained and tested using a total of 500 images of the five vowels. The method includes color information, to detect skin regions, hand segmentation, using morphological operations and filters, feature extraction in hand regions using PCA, and classification using SVM. A graphical user interface was implemented for real-time recognition. For this first approach, the system was optimized for working with the five vowels showing results of a testing accuracy above 80\% and an execution time of 59 milliseconds per frame.",

keywords = "Principal component analysis, sign language recognition, support vector machines",

author = "Cristian Amaya and Victor Murray",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 27th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020 ; Conference date: 03-09-2020 Through 05-09-2020",

year = "2020",

month = sep,

doi = "10.1109/INTERCON50315.2020.9220241",

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series = "Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020",

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Amaya, C & Murray, V 2020, Real-Time Sign Language Recognition. En Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020., 9220241, Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020, Institute of Electrical and Electronics Engineers Inc., 27th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020, Virtual, Lima, Perú, 3/09/20. https://doi.org/10.1109/INTERCON50315.2020.9220241

Real-Time Sign Language Recognition. / Amaya, Cristian; Murray, Victor.
Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9220241 (Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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T1 - Real-Time Sign Language Recognition

AU - Amaya, Cristian

AU - Murray, Victor

PY - 2020/9

Y1 - 2020/9

N2 - We propose an automatic system to recognize sign language using principal component analysis (PCA) and one-vs.-all support vector machines (SVM) classification. The algorithm was trained and tested using a total of 500 images of the five vowels. The method includes color information, to detect skin regions, hand segmentation, using morphological operations and filters, feature extraction in hand regions using PCA, and classification using SVM. A graphical user interface was implemented for real-time recognition. For this first approach, the system was optimized for working with the five vowels showing results of a testing accuracy above 80% and an execution time of 59 milliseconds per frame.

AB - We propose an automatic system to recognize sign language using principal component analysis (PCA) and one-vs.-all support vector machines (SVM) classification. The algorithm was trained and tested using a total of 500 images of the five vowels. The method includes color information, to detect skin regions, hand segmentation, using morphological operations and filters, feature extraction in hand regions using PCA, and classification using SVM. A graphical user interface was implemented for real-time recognition. For this first approach, the system was optimized for working with the five vowels showing results of a testing accuracy above 80% and an execution time of 59 milliseconds per frame.

KW - Principal component analysis

KW - sign language recognition

KW - support vector machines

UR - https://www.scopus.com/pages/publications/85095447245

U2 - 10.1109/INTERCON50315.2020.9220241

DO - 10.1109/INTERCON50315.2020.9220241

M3 - Conference contribution

AN - SCOPUS:85095447245

T3 - Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020

BT - Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 27th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020

Y2 - 3 September 2020 through 5 September 2020

ER -

Amaya C, Murray V. Real-Time Sign Language Recognition. En Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9220241. (Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020). doi: 10.1109/INTERCON50315.2020.9220241

Real-Time Sign Language Recognition

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